---
language:
- mn
base_model: bayartsogt/mongolian-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-ner-demo
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# roberta-base-ner-demo

This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mongolian-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1372
- Precision: 0.9235
- Recall: 0.9342
- F1: 0.9288
- Accuracy: 0.9800

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1652        | 1.0   | 477  | 0.0832          | 0.8915    | 0.9136 | 0.9024 | 0.9762   |
| 0.0512        | 2.0   | 954  | 0.0828          | 0.9071    | 0.9244 | 0.9156 | 0.9778   |
| 0.0268        | 3.0   | 1431 | 0.0909          | 0.9179    | 0.9274 | 0.9226 | 0.9787   |
| 0.0146        | 4.0   | 1908 | 0.0975          | 0.9217    | 0.9322 | 0.9269 | 0.9798   |
| 0.008         | 5.0   | 2385 | 0.1127          | 0.9178    | 0.9313 | 0.9245 | 0.9793   |
| 0.0053        | 6.0   | 2862 | 0.1255          | 0.9207    | 0.9295 | 0.9251 | 0.9790   |
| 0.0034        | 7.0   | 3339 | 0.1292          | 0.9235    | 0.9335 | 0.9285 | 0.9797   |
| 0.0024        | 8.0   | 3816 | 0.1339          | 0.9186    | 0.9332 | 0.9258 | 0.9795   |
| 0.0015        | 9.0   | 4293 | 0.1359          | 0.9239    | 0.9343 | 0.9291 | 0.9800   |
| 0.0011        | 10.0  | 4770 | 0.1372          | 0.9235    | 0.9342 | 0.9288 | 0.9800   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1